Best Beam Prediction in Non-Standalone mm Wave Systems

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We consider a machine learning approach to perform best beam prediction in Non-Standalone Millimeter Wave (mmWave) Systems utilizing Channel Charting (CC). The approach reduces communication overheads and delays associated
with initial access and beam tracking in 5G New Radio (NR) systems. The network has a mmWave and a sub-6 GHz component. We devise a Base Station (BS) centric approach for best mmWave beam prediction, based on Channel State Information (CSI) measured at the sub-6 GHz BS, with no need to exchange information with UEs. In a training phase, we collect CSI at the sub-6 GHz BS from sample UEs, and construct a dimensional reduction of the sample CSI, called a CC. We annotate the CC with best beam information measured at a mmWave BS for the sample UEs, assuming autonomous beamformer at the UE side. A beam predictor is trained based on this information, connecting any sub-6 GHz CSI with a predicted best mmWave beam. To evaluate the efficiency of the proposed framework, we perform simulations for a street segment with synthetic spatially consistent CSI. With a neural network predictor, we obtain 91% accuracy for predicting best beam and 99% accuracy for predicting one of two best beams. The accuracy of CC based beam prediction is indistinguishable from true location based beam prediction.
Original languageEnglish
Title of host publication2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)
Number of pages6
ISBN (Electronic)978-1-6654-1526-2
Publication statusPublished - 28 Jul 2021
MoE publication typeA4 Conference publication
EventEuropean Conference on Networks and Communications - Porto, Portugal
Duration: 8 Jun 202111 Jun 2021

Publication series

NameEuropean conference on networks and communications
ISSN (Print)2475-6490
ISSN (Electronic)2575-4912


ConferenceEuropean Conference on Networks and Communications
Abbreviated titleEuCNC


  • Non-Standalone systems
  • beam prediction
  • channel charting
  • network centric approach
  • radio resource management


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